Visual Saliency.
Visual saliency prediction has been well studied in computer vision [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}. We mainly review previous works on saliency prediction from videos. Wang et al. [2]} expanded image saliency models to videos by incorporating a new branch to handle temporal information. Wu et al. [4]} proposed SalSAC, which shuffles features of different CNN layers and feeds them to a correlation-based ConvLSTM. Wang et al. [9]} used multiple spatio-temporal self-attention modules to address the limitation of fixed kernel size in 3D models and to model long-range temporal dependencies. Chen et al. [10]} decomposed video saliency prediction into spatial pattern capture and spatio-temporal reasoning. Lou et al. [8]} combined a convolutional network and transformer architecture to model the long-range spatial context. While visual saliency prediction localizes interesting spatial regions as potential attention targets, egocentric gaze estimation seeks to determine the gaze target of the camera wearers as they interact with a scene. Moreover, the scene context captured from egocentric video is complex and rapidly changing, which requires a gaze estimation model with the ability of explicitly reasoning about the correlation between local visual features and global scene context. In our experiment section, we demonstrate that our proposed GLC module can significantly benefit gaze estimation performance under this challenging setting.
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